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A geometric view on inner transformation between the variables of a linear regression model
ID
Li, Zhaoyang
(
Author
),
ID
Antončič, Boštjan
(
Author
)
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https://www.scirp.org/journal/paperinformation.aspx?paperid=112812
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Abstract
In the teaching and researching of linear regression analysis, it is interesting and enlightening to explore how the dependent variable vector can be inner-transformed into regression coefficient estimator vector from a visible geometrical view. As an example, the roadmap of such inner transformation is presented based on a simple multiple linear regression model in this work. By applying the matrix algorithms like singular value decomposition (SVD) and Moore-Penrose generalized matrix inverse, the dependent variable vector lands into the right space of the independent variable matrix and is metamorphosed into regression coefficient estimator vector through the three-step of inner transformation. This work explores the geometrical relationship between the dependent variable vector and regression coefficient estimator vector as well as presents a new approach for vector rotating.
Language:
English
Keywords:
econometrics
,
regression analysis
,
matrix singular value decomposition
,
Moore-Penrose generalized inverse
,
matrix inner transformation
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
EF - School of Economics and Business
Publication status:
Published
Publication version:
Version of Record
Year:
2021
Number of pages:
Str. 931-938
Numbering:
Vol. 12, no. 10
PID:
20.500.12556/RUL-134026
UDC:
51-7
ISSN on article:
2152-7385
DOI:
10.4236/am.2021.1210061
COBISS.SI-ID:
83841027
Publication date in RUL:
22.12.2021
Views:
864
Downloads:
150
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Record is a part of a journal
Title:
Applied mathematics
Shortened title:
Appl. math.
Publisher:
Scientific Research Publishing, Inc.
ISSN:
2152-7385
COBISS.SI-ID:
1462364
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
29.10.2021
Secondary language
Language:
Slovenian
Keywords:
ekonometrija
,
regresijske analize
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